Package: lite 1.1.1

lite: Likelihood-Based Inference for Time Series Extremes

Performs likelihood-based inference for stationary time series extremes. The general approach follows Fawcett and Walshaw (2012) <doi:10.1002/env.2133>. Marginal extreme value inferences are adjusted for cluster dependence in the data using the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>, producing an adjusted log-likelihood for the model parameters. A log-likelihood for the extremal index is produced using the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292>. These log-likelihoods are combined to make inferences about extreme values. Both maximum likelihood and Bayesian approaches are available.

Authors:Paul J. Northrop [aut, cre, cph]

lite_1.1.1.tar.gz
lite_1.1.1.zip(r-4.7)lite_1.1.1.zip(r-4.6)lite_1.1.1.zip(r-4.5)
lite_1.1.1.tgz(r-4.6-any)lite_1.1.1.tgz(r-4.5-any)
lite_1.1.1.tar.gz(r-4.7-any)lite_1.1.1.tar.gz(r-4.6-any)
lite_1.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
lite/json (API)

# Install 'lite' in R:
install.packages('lite', repos = c('https://paulnorthrop.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/paulnorthrop/lite/issues

Pkgdown/docs site:https://paulnorthrop.github.io

On CRAN:

Conda:

clusteredextremal-indexextreme-value-statisticsextremesfrequentistgeneralised-paretoinferencelikelihoodlog-likelihoodthresholdtime-series

4.95 score 3 stars 15 scripts 246 downloads 7 exports 51 dependencies

Last updated from:67a081fb19. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK162
source / vignettesOK208
linux-release-x86_64OK164
macos-release-arm64OK144
macos-oldrel-arm64OK165
windows-develOK127
windows-releaseOK111
windows-oldrelOK120
wasm-releaseOK118

Exports:blitefitBernoullifitGPflitegpObsInfologLikVectorreturnLevel

Dependencies:abindbackportsbayesplotchandwichcheckmateclicpp11distributionaldplyrexdexfarvergenericsggplot2ggridgesgluegtableisobandlabelinglatticelifecyclemagrittrmatrixStatsnumDerivpillarpkgconfigplyrposteriorpurrrR6RColorBrewerRcppRcppArmadilloRcppRollreshape2revdbayesrlangrustS7sandwichscalesstringistringrtensorAtibbletidyrtidyselectutf8vctrsviridisLitewithrzoo

Frequentist Likelihood-Based Inference for Time Series Extremes
Cheeseboro wind gust data | Inferences for model parameters | Inferences for return levels | References
Rendered fromlite-1-frequentist.Rmdusingknitr::rmarkdown

Last update: 2023-01-26
Started: 2022-05-16

Bayesian Likelihood-Based Inference for Time Series Extremes
Bayesian inferences for model parameters | Predictive inference | References
Rendered fromlite-2-bayesian.Rmdusingknitr::rmarkdown

Last update: 2022-05-16
Started: 2022-05-16

Readme and manuals

Help Manual

Help pageTopics
lite: Likelihood-Based Inference for Time Series Extremeslite-package lite
Frequentist inference for the Bernoulli distributionBernoulli coef.Bernoulli fitBernoulli logLik.Bernoulli nobs.Bernoulli vcov.Bernoulli
Bayesian threshold-based inference for time series extremesblite
Methods for objects of class '"blite"'bliteMethods coef.blite confint.blite nobs.blite plot.blite print.summary.blite summary.blite vcov.blite
Functions for the 'estfun' methodestfun estfun.Bernoulli estfun.GP
Frequentist threshold-based inference for time series extremesflite
Methods for objects of class '"flite"'coef.flite confint.flite fliteMethods logLik.flite nobs.flite plot.flite print.summary.flite summary.flite vcov.flite
Frequentist inference for the generalised Pareto distributioncoef.GP fitGP generalisedPareto gpObsInfo logLik.GP nobs.GP vcov.GP
Functions for log-likelihood contributionslogLik.logLikVector logLikVector logLikVector.Bernoulli logLikVector.GP
Predictive inference for the largest value observed in N years.predict.blite
Frequentist threshold-based inference for return levelsreturnLevel
Methods for objects of class '"returnLevel"'plot.returnLevel print.returnLevel print.summary.returnLevel returnLevelMethods summary.returnLevel